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researcher

Jieping Ye

30 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author9
  • last author20

Across the 29 of 30 papers where every author was matched, so the position is known.

fields
  • cs.LG14
  • cs.CV7
  • cs.AI2
  • stat.ML2
  • cs.CL1
  • cs.DB1
ORCID 0000-0001-8662-5818
same name
  • Jieping Ye — 32 papers
  • Jieping Ye — 29 papers, h 96
  • Jieping Ye — 11 papers
  • Jieping Ye — 10 papers
  • Jieping Ye — 5 papers
  • Jieping Ye — 4 papers, h 7

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20102024
most citedMulti-Task Feature Learning Via Efficient l2,1-Norm Minimization

553 citations · 829 across the 30 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2024

Delving into the Reversal Curse: How Far Can Large Language Models Generalize?

Zhengkai Lin, Zhihang Fu, Kai Liu +6

While large language models (LLMs) showcase unprecedented capabilities, they also exhibit certain inherent limitations when facing seemingly trivial tasks. A prime example is the r…

cs.CL2024★ 1 cited

Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding

Deyi Ji, Lanyun Zhu, Siqi Gao +4

The ubiquity and value of tables as semi-structured data across various domains necessitate advanced methods for understanding their complexity and vast amounts of information. Des…

cs.CL2024

Enhancing Multiple Dimensions of Trustworthiness in LLMs via Sparse Activation Control

Yuxin Xiao, Chaoqun Wan, Yonggang Zhang +5

As the development and application of Large Language Models (LLMs) continue to advance rapidly, enhancing their trustworthiness and aligning them with human preferences has become…

cs.CL2024★ 3 cited

Instance-adaptive Zero-shot Chain-of-Thought Prompting

Xiaosong Yuan, Chen Shen, Shaotian Yan +6

Zero-shot Chain-of-Thought (CoT) prompting emerges as a simple and effective strategy for enhancing the performance of large language models (LLMs) in real-world reasoning tasks. N…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.